Abstract: Reverse engineering of full-genomic interaction networks based on compendia of expression data has been successfully applied for a number of model organisms. This study adapts these approaches for an important non-model organism: The major human fungal pathogen Candida albicans. During the infection process, the pathogen can adapt to a wide range of environmental niches and reversibly changes its growth form. Given the importance of these processes, it is important to know how they are regulated. This study presents a reverse engineering strategy able to infer fullgenomic interaction networks for C. albicans based on a linear regression, utilizing the sparseness criterion (LASSO). To overcome the limited amount of expression data and small number of known interactions, we utilize different prior-knowledge sources guiding the network inference to a knowledge driven solution. Since, no database of known interactions for C. albicans exists, we use a textmining system which utilizes full-text research papers to identify known regulatory interactions. By comparing with these known regulatory interactions, we find an optimal value for global modelling parameters weighting the influence of the sparseness criterion and the prior-knowledge. Furthermore, we show that soft integration of prior-knowledge additionally improves the performance. Finally, we compare the performance of our approach to state of the art network inference approaches.
Abstract: This paper focuses on robust design and optimization
of industrial production wastes. Past literatures were reviewed to case
study Clamason Industries Limited (CIL) - a leading ladder-tops
manufacturer. A painstaking study of the firm-s practices at the shop
floor revealed that Over-production, Waiting time, Excess inventory,
and Defects are the major wastes that are impeding their progress and
profitability. Design expert8 software was used to apply Taguchi
robust design and response surface methodology in order to model,
analyse and optimise the wastes cost in CIL. Waiting time and overproduction
rank first and second in contributing to the costs of wastes
in CIL. For minimal wastes cost the control factors of overproduction,
waiting-time, defects and excess-inventory must be set at
0.30, 390.70, 4 and 55.70 respectively for CIL. The optimal value of
cost of wastes for the months studied was 22.3679. Finally, a
recommendation was made that for the company to enhance their
profitability and customer satisfaction, they must adopt the Shingeo
Shingo-s Single Minute Exchange of Dies (SMED), which will
immediately tackle the waste of waiting by drastically reducing their
setup time.
Abstract: Most routing protocols (DSR, AODV etc.) that have
been designed for wireless adhoc networks incorporate the broadcasting
operation in their route discovery scheme. Probabilistic broadcasting
techniques have been developed to optimize the broadcast operation
which is otherwise very expensive in terms of the redundancy
and the traffic it generates. In this paper we have explored percolation
theory to gain a different perspective on probabilistic broadcasting
schemes which have been actively researched in the recent years.
This theory has helped us estimate the value of broadcast probability
in a wireless adhoc network as a function of the size of the network.
We also show that, operating at those optimal values of broadcast
probability there is at least 25-30% reduction in packet regeneration
during successful broadcasting.
Abstract: Estimation of voltage stability based on optimal
filtering method is presented. PV curve is used as a tool for voltage stability analysis. Dynamic voltage stability estimation is done by
using particle filter method. Optimum value (nose point) of PV curve can be estimated by estimating parameter of PV curve equation
optimal value represents critical voltage and
condition at specified point of measurement. Voltage stability is then estimated by analyzing loading margin condition c stimating equation. This
maximum loading
ecified dynamically.
Abstract: The use of power system stabilizers (PSSs) to damp
power system swing mode of oscillations is practical important. Our
purpose is to retune the power system stabilizer (PSS1A) parameters
in Unitrol D produced by ABB– was installed in 1995in Benghazi
North Power Plants (BNPPs) at General Electricity Company of
Libya (GECOL). The optimal values of the power system stabilizer
(PSS1A) parameters are determined off-line by a particle swarm
optimization technique (PSO). The objective is to damp the local and
inter-area modes of oscillations that occur following power system
disturbances. The retuned power system stabilizer (PSS1A) can cope
with large disturbance at different operating points and has enhanced
power system stability.
Abstract: This study proposes a hybrid minimal repair policy
which combines periodic maintenance policy with age-based maintenance policy for a serial production system. Parameters of such policy are defined as and which indicate as hybrid minimal
repair time and planned preventive maintenance time
respectively . Under this hybrid policy, the system is
repaired minimally if it fails during , . A perfect repair is
conducted on the first failure after at any machines. At the same time, we take opportunity to advance the preventive maintenance of
other machines simultaneously. If the system is still operating
properly up to , then the preventive maintenance is carried out as its
predetermined schedule. For a given , we obtain the optimal value which minimizes the expected cost per time unit. Numerical
example is presented to illustrate the properties of the optimal solution.
Abstract: This paper evaluates the dividend payments for general
claim size distributions in the presence of a dividend barrier. The
surplus of a company is modeled using the classical risk process
perturbed by diffusion, and in addition, it is assumed to accrue interest
at a constant rate. After presenting the integro-differential equation
with initial conditions that dividend payments satisfies, the paper
derives a useful expression of the dividend payments by employing
the theory of Volterra equation. Furthermore, the optimal value of
dividend barrier is found. Finally, numerical examples illustrate the
optimality of optimal dividend barrier and the effects of parameters
on dividend payments.
Abstract: Border Gateway Protocol (BGP) is the standard routing protocol between various autonomous systems (AS) in the internet. In the event of failure, a considerable delay in the BGP convergence has been shown by empirical measurements. During the convergence time the BGP will repeatedly advertise new routes to some destination and withdraw old ones until it reach a stable state. It has been found that the KEEPALIVE message timer and the HOLD time are tow parameters affecting the convergence speed. This paper aims to find the optimum value for the KEEPALIVE timer and the HOLD time that maximally reduces the convergence time without increasing the traffic. The KEEPALIVE message timer optimal value founded by this paper is 30 second instead of 60 seconds, and the optimal value for the HOLD time is 90 seconds instead of 180 seconds.
Abstract: This paper presents a novel two-phase hybrid optimization algorithm with hybrid genetic operators to solve the optimal control problem of a single stage hybrid manufacturing system. The proposed hybrid real coded genetic algorithm (HRCGA) is developed in such a way that a simple real coded GA acts as a base level search, which makes a quick decision to direct the search towards the optimal region, and a local search method is next employed to do fine tuning. The hybrid genetic operators involved in the proposed algorithm improve both the quality of the solution and convergence speed. The phase–1 uses conventional real coded genetic algorithm (RCGA), while optimisation by direct search and systematic reduction of the size of search region is employed in the phase – 2. A typical numerical example of an optimal control problem with the number of jobs varying from 10 to 50 is included to illustrate the efficacy of the proposed algorithm. Several statistical analyses are done to compare the validity of the proposed algorithm with the conventional RCGA and PSO techniques. Hypothesis t – test and analysis of variance (ANOVA) test are also carried out to validate the effectiveness of the proposed algorithm. The results clearly demonstrate that the proposed algorithm not only improves the quality but also is more efficient in converging to the optimal value faster. They can outperform the conventional real coded GA (RCGA) and the efficient particle swarm optimisation (PSO) algorithm in quality of the optimal solution and also in terms of convergence to the actual optimum value.
Abstract: A novel path planning approach is presented to solve
optimal path in stochastic, time-varying networks under priori traffic
information. Most existing studies make use of dynamic programming
to find optimal path. However, those methods are proved to
be unable to obtain global optimal value, moreover, how to design
efficient algorithms is also another challenge.
This paper employs a decision theoretic framework for defining
optimal path: for a given source S and destination D in urban transit
network, we seek an S - D path of lowest expected travel time
where its link travel times are discrete random variables. To solve
deficiency caused by the methods of dynamic programming, such as
curse of dimensionality and violation of optimal principle, an integer
programming model is built to realize assignment of discrete travel
time variables to arcs. Simultaneously, pruning techniques are also
applied to reduce computation complexity in the algorithm. The final
experiments show the feasibility of the novel approach.